class_docstr: Find a linear mapping from uncalibrated activations to true activations.
  
  Should not change ev_correlation_score because it is invariant to linear transformations.
code: def apply_calibration(self, values: Sequence[float]) -> list[float]:
      if self._regression is None:
          raise ValueError(
              "Must call calibrate() before apply_calibration"
          )
      if len(values) == 0:
          return []
      return self._regression.predict(
          np.reshape(np.array(values), (-1, 1))
      ).tolist()
  
imports: from __future__ import annotations
  import asyncio
  from abc import abstractmethod
  from typing import Optional, Sequence
  import numpy
  from neuron_explainer.activations.activations import ActivationRecord
  from neuron_explainer.explanations.explanations import ActivationScale
  from neuron_explainer.explanations.simulator import NeuronSimulator, SequenceSimulation
  from sklearn import linear_model
label_desc: Use image node_id 3 for calling the LinearCalibratedNeuronSimulator obj's underlying member method code with example usage: obj.apply_calibration(values) and returns: self, list